Large-scale inference of the transcriptional regulation of Bacillus subtilis

Gupta, Amit Kumar; Varner, Jeffrey D.; Maranas, Costas D.

doi:10.1016/j.compchemeng.2004.08.030

Cited by 21 publications

(30 citation statements)

References 77 publications

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“…This results in an extremely high‐dimensional search space. For such large‐scale reverse engineering, statistical methods 2 or regression techniques relying on relatively simple dynamical models based on first‐order approximations of gene expression dynamics 3,4 are typically used. Here, we are interested in inference methods that target smaller networks, but use more accurate and biologically plausible, nonlinear gene network models.…”

Section: Introductionmentioning

confidence: 99%

“…These technologies are advancing at a fast pace. As the quality of available data improves, we believe it timely to explore the possibility of using more detailed phenomenological model types-enabling a more faithful reconstruction of the gene network-than commonly used models of gene regulation such as the linear model, [5][6][7] the log-linear model, 3,4,8 the sigmoid model, [9][10][11][12][13] or S-Systems. 14,15 As a first step in this direction, we introduce a log-sigmoid gene network model that can Evolutionary and functional constraints shape the "design space" of biological networks, 31 which often have design features such as sparseness, network motifs, robustness.…”

Section: Introductionmentioning

confidence: 99%

“…16 This is especially important when the reverse-engineering problem is underdetermined by the available data. 17 Because biological networks are generally sparse, most state-ofthe-art reverse-engineering methods include an explicit bias toward sparse networks, 3,6,7,11,12 for example by limiting the maximum number of connections per gene.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Replaying the Evolutionary Tape: Biomimetic Reverse Engineering of Gene Networks

Marbach

Mattiussi

Floreano

2009

Annals of the New York Academy of Sciences

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In this paper, we suggest a new approach for reverse engineering gene regulatory networks, which consists of using a reconstruction process that is similar to the evolutionary process that created these networks. The aim is to integrate prior knowledge into the reverse engineering procedure, thus biasing the search towards biologically plausible solutions. To this end, we propose an evolutionary method that abstracts and mimics the natural evolution of gene regulatory networks. Our method can be used with a wide range of nonlinear dynamical models. This allows us to explore novel model types such as the log-sigmoid model introduced here. We apply the biomimetic method to a gold standard dataset from an in vivo gene network. The obtained results won a reverse engineering competition of the second DREAM conference.DREAM challenge | gene regulatory networks | reverse engineering | prior knowledge

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Replaying the Evolutionary Tape: Biomimetic Reverse Engineering of Gene Networks

Marbach

Mattiussi

Floreano

2009

Annals of the New York Academy of Sciences

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“…However, with the typically noisy and relatively small datasets available, there clearly are many different networks that are consistent with the data. Some methods identify a unique “best” network from this ensemble according to some additional criteria, 1–3 for example by posing constraints on the connectivity of the network (Fig. 1A).…”

Section: Introductionmentioning

confidence: 99%

Combining Multiple Results of a Reverse‐Engineering Algorithm: Application to the DREAM Five‐Gene Network Challenge

Marbach¹,

Mattiussi²,

Floreano³

2009

Annals of the New York Academy of Sciences

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The output of reverse engineering methods for biological networks is often not a single network prediction, but an ensemble of networks that are consistent with the experimentally measured data. In this paper, we consider the problem of combining the information contained within such an ensemble in order to (1) make more accurate network predictions and (2) estimate the reliability of these predictions. We review existing methods, discuss their limitations, and point out possible research directions towards more advanced methods for this purpose.

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“…Experimental data on gene expression levels is substituted into the relational equations, and the ensuing system of equations is then solved for the regulatory relationships between two or more components ( Figure 1). Because often there are far more biochemical components in the network than there are experimental time points, multiple networks will be possible solutions; these are filtered by making plausible assumptions on the objectives of the underlying system, such as economy of regulation (reflected by having the fewest edges that satisfy the conditions) or maximal biomass production flux (Gupta et al, 2005). A recent plant biology application of this method used microarray data to infer circadian regulatory pathways in Arabidopsis.…”

Section: Inference Of Interaction Network From Expression Informationmentioning

confidence: 99%

Network Inference, Analysis, and Modeling in Systems Biology

2007

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Cells use signaling and regulatory pathways connecting numerous constituents, such as DNA, RNA, proteins, and small molecules, to coordinate multiple functions, allowing them to adapt to changing environments. High-throughput experimental methods enable the measurement of expression levels for thousands of genes and the determination of thousands of protein-protein or protein-DNA interactions. It is increasingly recognized that theoretical methods, such as statistical inference, graph analysis, and dynamic modeling, are needed to make sense of this abundance of information. This perspective argues that theoretical methods and models are most useful if they lead to novel biological predictions and reviews biological predictions arising from three systems biology topics: graph inference (i.e., reconstructing the network of interactions among a set of biological entities), graph analysis (i.e., mining the information content of the network), and dynamic network modeling (i.e., connecting the interaction network to the dynamic behavior of the system). The methods and principles discussed in this perspective are generally applicable, and the examples were selected from plant biology wherever possible. INTRODUCTIONTo understand the function of a cell or of higher units of biological organization, often it is beneficial to conceptualize them as systems of interacting elements. For such a systems-level description (which represents the main goal of systems biology), one needs to know (1) the identity of the components that constitute the biological system; (2) the dynamic behavior of these components (i.e., how their abundance or activity changes over time in various conditions); and (3) the interactions among these components (Kitano, 2002). Ultimately, this information can be combined into a model that is not only consistent with current knowledge but provides new insights and predictions, such as the behavior of the system in conditions that were previously unexplored.The origins of systems biology can be traced back to systems theory, a line of inquiry based on the assumptions that all phenomena can be viewed as a web of relationships among elements, and all systems can be handled by a common set of methods (von Bertalanffy, 1968;Weinberg, 1975;Bogdanov, 1980;Heinrich and Schuster, 1996;Francois, 1999;Voit, 2000). Early attempts at systems-level understanding of biology suffered from inadequate data on which to base the theories and models; however, the recent advent of high-throughput technologies brought an abundance of data on system elements and interactions, leading to a revival of systems biology.In some cases, the organization of the network of interactions underlying a biological system is straightforward (e.g., a linear chain of interactions), while in other cases a more formal representation, offered by mathematical graph theory (Bollobá s, 1979), is required. The simplest possible graph representation reduces the system's elements to graph nodes (also called vertices) and reduces their pairwise relationsh...

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Large-scale inference of the transcriptional regulation of Bacillus subtilis

Cited by 21 publications

References 77 publications

Replaying the Evolutionary Tape: Biomimetic Reverse Engineering of Gene Networks

Replaying the Evolutionary Tape: Biomimetic Reverse Engineering of Gene Networks

Combining Multiple Results of a Reverse‐Engineering Algorithm: Application to the DREAM Five‐Gene Network Challenge

Network Inference, Analysis, and Modeling in Systems Biology

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